Evaluating the Feasibility of RAG Models for Regulatory Compliance in Investment Management
Prof. Jesús Martínez del Rincón
Mr. Abhishek Pramanick
Dr. Barry Quinn
Project Summary
This project, led by Dr. Barry Quinn and Dr. Jesús Martínez del Rincón from Queen’s University Belfast, aims to create an AI framework to simplify and enhance regulatory compliance in global investment management.
The research focuses on Retrieval-Augmented Generation (RAG) and ontology learning algorithms to transform complex regulatory texts into clear, consistent rules reflecting current international standards.
Study Period 1/11/2024 - 31/10/2024
Key Objectives
Explore the advantages of AI in regulatory compliance for the investment management sector.
Evaluate AI’s impact on accuracy, efficiency, and cost-effectiveness.
Focus on key areas like regulatory reporting, risk assessments, and compliance monitoring.
Address challenges in using Large Language Models (LLMs), including hallucinations, reasoning, and auditability.
Funding: UKRI through the UKFin+ program for a 12-month period; FEC £99,871 (Nov 2024 - Oct 2025).
Industrial Partner: Funds Axis Ltd provides support and industry insights.
Work Package 1: Use Case Definition, Data Collection & Process Mapping
Regulatory Focus
Codification of Investment Regulations:
Focus on EFAMA EFC Categories.
Rules for portfolio composition (e.g., asset class restrictions on equities, bonds, and country/industry limits).
Compare regulatory limits with existing portfolios to ensure compliance.
Document Analysis:
Use RAG to analyse multiple versions of regulatory texts for overlaps, inconsistencies, and conflicting rules.
Proposed Use Cases
Portfolio Compliance:
Leverage Funds Axis’s classified spreadsheet for model training.
Train models to validate portfolio adherence to regulations.
Identify conflicting rules across versions.
Advanced Legal Analysis:
Address complex regulations from authorities like the FCA.
Extract logic from dense legal language for future use cases.